Simultaneous localization and mapping architecture for small bodies and space exploration

Abstract

The development of autonomous robotic platforms for space applications such as satellites, robotic manipulators and rovers has driven the need for reliable control and navigation techniques. Simultaneous Localization and Mapping (SLAM) algorithms can be employed to determine the position and orientation of robotic platforms while generating a map of an unknown hostile environment. This article presents the successful integration of ORB-SLAM2, a localization algorithm, with Voxblox, an accurate three-dimensional mapping package. This integration aims to establish a pathway for occupancy maps to be generated from feature detection algorithms. Furthermore, virtual reality is presented as an innovative solution for testing the performance of this algorithm integration in a space environment. Providing a novel simulation environment with an opportunity to diversify applications. The effectiveness of this integration and SLAM accuracy was compared to a truth model extracted from the virtual reality environment. The accuracy is dependent on the number of observed and matched features in a given frame and are intrinsic characteristic of features in the environment. The robustness of the integration is also examined through the implementation of sensor inputs in the form of stereo and RGB-D cameras. The algorithms’ integration and embedding into virtual reality allows for further developments of SLAM algorithms, to improve accuracy and robustness for autonomous navigation in any virtual environment, and extend robotic simulations and visualizations.